Network connectivity underlying episodic memory in children: Application of a pediatric brain tumor survivor injury model
Bibliographic record
Abstract
Episodic memory involves personal experiences paired with their context. The Medial Temporal, Posterior Medial, Anterior Temporal, and Medial Prefrontal networks have been found to support the hippocampus in episodic memory in adults. However, there lacks a model that captures how the structural and functional connections of these networks interact to support episodic memory processing in children. Using diffusion-weighted imaging, magnetoencephalography, and memory tests, we quantified differences in white matter microstructure, neural communication, and episodic memory performance, respectively, of healthy children (n = 23) and children with reduced memory performance. Pediatric brain tumor survivors (PBTS; n = 24) were used as a model, as they exhibit reduced episodic memory and perturbations in white matter and neural communication. We observed that PBTS, compared to healthy controls, showed significantly (p < 0.05) (1) disrupted white matter microstructure between these episodic memory networks through lower fractional anisotropy and higher mean and axial diffusivity, (2) perturbed theta band (4-7 Hz) oscillatory synchronization in these same networks through higher weighted phase lag indices (wPLI), and (3) lower episodic memory performance in the Transverse Patterning and Children's Memory Scale (CMS) tasks. Using partial-least squares path modeling, we found that brain tumor treatment predicted network white matter damage, which predicted inter-network theta hypersynchrony and lower verbal learning (directly) and lower verbal recall (indirectly via theta hypersynchrony). Novel to the literature, our findings suggest that white matter modulates episodic memory through effect on oscillatory synchronization within relevant brain networks. RESEARCH HIGHLIGHTS: Investigates the relationship between structural and functional connectivity of episodic memory networks in healthy children and pediatric brain tumor survivors Pediatric brain tumor survivors demonstrate disrupted episodic memory, white matter microstructure and theta oscillatory synchronization compared to healthy children Findings suggest white matter microstructure modulates episodic memory through effects on oscillatory synchronization within relevant episodic memory networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".